python - Pytorch 不通过迭代张量构造进行反向传播

Question

我目前正在尝试在 Pytorch 中迭代构建张量。遗憾的是，反向传播不适用于循环中的就地操作。例如，我已经尝试过使用堆栈的等效程序。有人知道我如何使用可用的反向传播来构建张量吗？

这是一个产生错误的最小示例:

import torch

k=2
a =torch.Tensor([10,20])
a.requires_grad_(True)
b = torch.Tensor([10,20])
b.requires_grad_(True)

batch_size = a.size()[0]
uniform_samples = Uniform(torch.tensor([0.0]), torch.tensor([1.0])).rsample(torch.tensor([batch_size,k])).view(-1,k)
exp_a = 1/a
exp_b = 1/b
km = (1- uniform_samples.pow(exp_b)).pow(exp_a)

sticks = torch.zeros(batch_size,k)
remaining_sticks = torch.ones_like(km[:,0])
for i in range(0,k-1):
    sticks[:,i] = remaining_sticks * km[:,i]
    remaining_sticks *= (1-km[:,i])
sticks[:,k-1] = remaining_sticks
latent_variables = sticks

latent_variables.sum().backward()

堆栈跟踪:

/opt/conda/conda-bld/pytorch_1570910687230/work/torch/csrc/autograd/python_anomaly_mode.cpp:57: UserWarning: Traceback of forward call that caused the error:
  File "/opt/conda/lib/python3.6/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/opt/conda/lib/python3.6/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py", line 16, in <module>
    app.launch_new_instance()
  File "/opt/conda/lib/python3.6/site-packages/traitlets/config/application.py", line 664, in launch_instance
    app.start()
  File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelapp.py", line 563, in start
    self.io_loop.start()
  File "/opt/conda/lib/python3.6/site-packages/tornado/platform/asyncio.py", line 148, in start
    self.asyncio_loop.run_forever()
  File "/opt/conda/lib/python3.6/asyncio/base_events.py", line 438, in run_forever
    self._run_once()
  File "/opt/conda/lib/python3.6/asyncio/base_events.py", line 1451, in _run_once
    handle._run()
  File "/opt/conda/lib/python3.6/asyncio/events.py", line 145, in _run
    self._callback(*self._args)
  File "/opt/conda/lib/python3.6/site-packages/tornado/ioloop.py", line 690, in <lambda>
    lambda f: self._run_callback(functools.partial(callback, future))
  File "/opt/conda/lib/python3.6/site-packages/tornado/ioloop.py", line 743, in _run_callback
    ret = callback()
  File "/opt/conda/lib/python3.6/site-packages/tornado/gen.py", line 787, in inner
    self.run()
  File "/opt/conda/lib/python3.6/site-packages/tornado/gen.py", line 748, in run
    yielded = self.gen.send(value)
  File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 361, in process_one
    yield gen.maybe_future(dispatch(*args))
  File "/opt/conda/lib/python3.6/site-packages/tornado/gen.py", line 209, in wrapper
    yielded = next(result)
  File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 268, in dispatch_shell
    yield gen.maybe_future(handler(stream, idents, msg))
  File "/opt/conda/lib/python3.6/site-packages/tornado/gen.py", line 209, in wrapper
    yielded = next(result)
  File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 541, in execute_request
    user_expressions, allow_stdin,
  File "/opt/conda/lib/python3.6/site-packages/tornado/gen.py", line 209, in wrapper
    yielded = next(result)
  File "/opt/conda/lib/python3.6/site-packages/ipykernel/ipkernel.py", line 300, in do_execute
    res = shell.run_cell(code, store_history=store_history, silent=silent)
  File "/opt/conda/lib/python3.6/site-packages/ipykernel/zmqshell.py", line 536, in run_cell
    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
  File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2855, in run_cell
    raw_cell, store_history, silent, shell_futures)
  File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2881, in _run_cell
    return runner(coro)
  File "/opt/conda/lib/python3.6/site-packages/IPython/core/async_helpers.py", line 68, in _pseudo_sync_runner
    coro.send(None)
  File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 3058, in run_cell_async
    interactivity=interactivity, compiler=compiler, result=result)
  File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 3249, in run_ast_nodes
    if (await self.run_code(code, result,  async_=asy)):
  File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 3326, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-124-2bbdbc3af797>", line 16, in <module>
    sticks[:,i] = remaining_sticks * km[:,i]

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-124-2bbdbc3af797> in <module>
     19 latent_variables = sticks
     20 
---> 21 latent_variables.sum().backward()

/opt/conda/lib/python3.6/site-packages/torch/tensor.py in backward(self, gradient, retain_graph, create_graph)
    148                 products. Defaults to ``False``.
    149         """
--> 150         torch.autograd.backward(self, gradient, retain_graph, create_graph)
    151 
    152     def register_hook(self, hook):

/opt/conda/lib/python3.6/site-packages/torch/autograd/__init__.py in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables)
     97     Variable._execution_engine.run_backward(
     98         tensors, grad_tensors, retain_graph, create_graph,
---> 99         allow_unreachable=True)  # allow_unreachable flag
    100 
    101 

RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.FloatTensor [2]] is at version 1; expected version 0 instead. Hint: the backtrace further above shows the operation that failed to compute its gradient. The variable in question was changed in there or anywhere later. Good luck!

Answer 1

您无法进行任何就地操作。因此，您不得在算法中使用 *=。

k = 2
a = torch.tensor(np.array([10.,20]), requires_grad=True).float()
b = torch.tensor(np.array([10.,20]), requires_grad=True).float()

batch_size = a.size()[0]
uniform_samples = Uniform(torch.tensor([0.]), torch.tensor([1.])).rsample(torch.tensor([batch_size,k])).view(-1,k)
exp_a = 1/a
exp_b = 1/b
km = (1 - uniform_samples**exp_b)**exp_a

sticks = torch.zeros(batch_size,k)
remaining_sticks = torch.ones_like(km[:,0])
for i in range(0,k-1):
    sticks[:,i] = remaining_sticks * km[:,i]
    remaining_sticks = remaining_sticks * (1-km[:,i])
sticks[:,k-1] = remaining_sticks
latent_variables = sticks
latent_variables = torch.sum(latent_variables)

latent_variables.backward()